tidytlg
A tables-listings-graphs package that reached CRAN and then went quiet.
A side-by-side editorial comparison of datasetjson and metatools — release velocity, themes, recent moves, and the top alternatives to consider.
datasetjson rebuilt its object model to track the CDISC Dataset-JSON 1.1 schema.
datasetjson reads and writes CDISC Dataset-JSON, the JSON replacement for SAS transport files in clinical-trial submissions. The package went from a thin reader in 2023 to a redesigned interface in 0.3.0 that targets the 1.1.0 schema, uses yyjsonr as its JSON backend, and exposes column metadata as first-class arguments. Development is contributor-driven inside the Atorus and pharmaverse orbit.
SDTM supplemental-qualifier merging got sturdier, then the package went quiet for two years.
metatools provides the utilities that build and check SDTM and ADaM datasets against their metadata in the pharmaverse. The 0.1.6 release in July 2024 is the substantive one: combine_supp() learned to handle zero-row supplemental data, to refuse QNAM columns already present in the source, and to route multiple QNAM values to the same IDVAR, alongside enhanced controlled-terminology checks and record-uniqueness verification. Nothing has shipped since.
datasetjson reads and writes CDISC Dataset-JSON, the JSON replacement for SAS transport files in clinical-trial submissions. The package went from a thin reader in 2023 to a redesigned interface in 0.3.0 that targets the 1.1.0 schema, uses yyjsonr as its JSON backend, and exposes column metadata as first-class arguments. Development is contributor-driven inside the Atorus and pharmaverse orbit.
The package's roadmap is not its own — it tracks a CDISC standard that is still moving, and 0.3.0 is what happens when the standard revises: object model, read and write paths, and JSON backend all changed together. Performance was addressed in the same pass, which matters because submission datasets are large enough that a slow serialiser is a real constraint.
The next significant release will most likely follow the next Dataset-JSON schema revision rather than an internal roadmap, given that 0.3.0 was driven entirely by the 1.1.0 update.
metatools provides the utilities that build and check SDTM and ADaM datasets against their metadata in the pharmaverse. The 0.1.6 release in July 2024 is the substantive one: combine_supp() learned to handle zero-row supplemental data, to refuse QNAM columns already present in the source, and to route multiple QNAM values to the same IDVAR, alongside enhanced controlled-terminology checks and record-uniqueness verification. Nothing has shipped since.
The package's development has been concentrated on one function, combine_supp(), which is where the messy realities of supplemental qualifiers surface — whitespace in join keys, empty supp datasets, colliding names. 0.1.6 also drew three first-time contributors, which is the healthiest signal in the history, but no release has followed. Sibling packages have meanwhile been dropping metatools as a dependency.
Without a release in two years the package looks stable rather than active; the plausible trigger is a controlled-terminology or dplyr change that forces the checks to be updated.
Other Analytics products tracked by Sparkpulse, ranked by recent ship velocity. Each card links to a full editorial trajectory and lets you pivot into a head-to-head comparison with either datasetjson or metatools.
A tables-listings-graphs package that reached CRAN and then went quiet.
Tplyr made clinical summary tables explain where every number came from.
Clinical listings that keep inheriting their hardest problem — pagination — from the layer below.
A cache-directory helper that has shipped nothing but CRAN-triggered patches for seven years.
gigs redesigned its whole conversion API for rOpenSci, then spent three releases getting the docs to build.
A weather-data client that keeps rewriting its HTTP layer while slowly tightening its API.
See all datasetjson alternatives → · See all metatools alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package, pharmaverse — within Analytics. datasetjson and metatools are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. datasetjson and metatools are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
Top datasetjson alternatives in Analytics are ranked by recent ship velocity. Browse the "datasetjson alternatives" section above for the current picks, or visit /alternatives/datasetjson for the full list with editorial commentary on each.
Top metatools alternatives in Analytics are ranked by recent ship velocity. Browse the "metatools alternatives" section above for the current picks, or visit /alternatives/metatools for the full list with editorial commentary on each.